Somatic structure variations (SVs) and copy number variations (CNVs) may result in genetic changes that are directly or indirectly related to different types of neoplasm. Computational tools have been developed to detect structural and copy number variations from next-generation sequencing (NGS) data. However, with no prior knowledge about variants in real samples, those tools have been hindered by the lack of a gold standard benchmark.
VarSimLab is a Docker-based package that generates artificial short reads, which harbor structural and copy number variations. With a web-based interface running locally, it not only provides a convenient way of using the package, but also runs efficiently generating data where it will be finally analyzed. In addition, VarSimLab comes with a visualization module to induce a better understanding of the generated data.
Check out the project on GitHub: https://github.com/NabaviLab/EasySCNVSim